Machine Learning Framework for Classification in Medicine and Biology

Lee, Eva K.

doi:10.1007/978-3-642-01929-6_1

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…When the prediction is categorical, the common set of machine learning tools are “classifiers”. We have applied DAMIP (discriminant analysis – mixed integer program [54]) classifier to our yellow fever and flu studies [12, 15], while many other classifiers have been applied or developed in the context of omics, including Linear discrimination analysis (LDA [55]), K-nearest neighbors (KNN [56]), partitioning around medoids (PAM [57]), Support Vector Machines [58] and Random Forest [59]. The “rules” in some classifiers are easy to interpret, e.g.…”

Section: Data Handling Analysis and Modelingmentioning

confidence: 99%

“…This will also be the case in vaccine studies, where it is often difficult to define a good or poor response. An advantage of the DAMIP method used in Querec et al [15] and Nakaya et al [12] is that it has added tolerance to errors in class definition [54]. The MAQC report also pointed out that multiple classifiers can achieve equally good results, and good practice is often more important than the classification methods being used [61].…”

Section: Data Handling Analysis and Modelingmentioning

confidence: 99%

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Systems biological approaches to measure and understand vaccine immunity in humans

Li¹,

Nakaya²,

Kazmin³

et al. 2013

Seminars in Immunology

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Recent studies have demonstrated the utility of using systems approaches to identify molecular signatures that can be used to predict vaccine immunity in humans. Such approaches are now being used extensively in vaccinology, and are beginning to yield novel insights about the molecular networks driving vaccine immunity. In this review, we present a broad review of the methodologies involved in these studies, and discuss the promise and challenges involved in this emerging field of “systems vaccinology.”

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Section: Data Handling Analysis and Modelingmentioning

confidence: 99%

Section: Data Handling Analysis and Modelingmentioning

confidence: 99%

Systems biological approaches to measure and understand vaccine immunity in humans

Li¹,

Nakaya²,

Kazmin³

et al. 2013

Seminars in Immunology

View full text Add to dashboard Cite

show abstract

“…Classification methods have been commonly employed to discover and validate sets of biomarkers in system-wide biomedical studies that fulfil predefined performance metrics, demonstrate clinical utility, and meet technical, practical clinical, and business-related expectations, which permit pursuing the development and commercialization of a clinical assay. Those studies frequently aid the prognostic and diagnostic assessment, and the predictive comparison of treatments of diseases such as cancer [ 10 , 11 ], liver [ 12 ], or neurodegenerative diseases [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Effects of Pooling Samples on the Performance of Classification Algorithms: A Comparative Study

Kusonmano

Netzer

Baumgartner³

et al. 2012

The Scientific World Journal

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A pooling design can be used as a powerful strategy to compensate for limited amounts of samples or high biological variation. In this paper, we perform a comparative study to model and quantify the effects of virtual pooling on the performance of the widely applied classifiers, support vector machines (SVMs), random forest (RF), k-nearest neighbors (k-NN), penalized logistic regression (PLR), and prediction analysis for microarrays (PAMs). We evaluate a variety of experimental designs using mock omics datasets with varying levels of pool sizes and considering effects from feature selection. Our results show that feature selection significantly improves classifier performance for non-pooled and pooled data. All investigated classifiers yield lower misclassification rates with smaller pool sizes. RF mainly outperforms other investigated algorithms, while accuracy levels are comparable among all the remaining ones. Guidelines are derived to identify an optimal pooling scheme for obtaining adequate predictive power and, hence, to motivate a study design that meets best experimental objectives and budgetary conditions, including time constraints.

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“…The idea is to learn a model from labeled samples that represent experiences, and predict group membership for newly registered data instances. Classification technique finds applications in a wide range of commercial and scientific human activites including manufacturing [4], medicine [5], finance [6], astronomy [7] and bioinformatics [8].…”

Section: Introductionmentioning

confidence: 99%

An efficient classifier ensemble using SVM

Bhardwaj

Gupta

Grover

et al. 2009

2009 Proceeding of International Conference on Methods and Models in Computer Science (ICM2CS)

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The diversity of base learners is of utmost importance to a good ensemble. This paper defines a novel measurement of diversity, termed as exclusivity. With the designed exclusivity, we further propose an ensemble SVM classifier, namely Exclusivity Regularized Machine (ExRM), to jointly suppress the training error of ensemble and enhance the diversity between bases. Moreover, an Augmented Lagrange Multiplier based algorithm is customized to effectively and efficiently seek the optimal solution of ExRM. Theoretical analysis on convergence , global optimality and linear complexity of the proposed algorithm, as well as experiments are provided to reveal the efficacy of our method and show its superiority over state-of-the-arts in terms of accuracy and efficiency.

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Machine Learning Framework for Classification in Medicine and Biology

Cited by 10 publications

References 11 publications

Systems biological approaches to measure and understand vaccine immunity in humans

Systems biological approaches to measure and understand vaccine immunity in humans

Effects of Pooling Samples on the Performance of Classification Algorithms: A Comparative Study

An efficient classifier ensemble using SVM

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